解决对称3D形状的方向估计难题，提升姿态对齐精度
Symmetry-Robust 3D Orientation Estimation
- 分两阶段设计，针对旋转对称性问题优化方向估计
- 在Shapenet全集上实现顶尖的竖直轴估计性能
- 适合需要高精度3D姿态对齐的研究与应用
方向估计是3D形状分析中的基础任务，旨在确定形状的侧向、竖直和前向轴。通过该信息可将形状旋转至标准朝向，使其方向轴与坐标轴对齐。目前，可靠估计一般形状完整方向仍是一个开放问题。本文提出一种两阶段方向估计流程，在竖直轴估计上达到当前最优性能，并进一步验证了其在完整方向估计（即三个方向轴）上的有效性。不同于以往研究仅在部分类别上训练与评估，本方法在Shapenet全部类别上进行训练与测试。我们从理论上分析旋转对称形状方向估计的根本障碍，并说明所提方法如何规避这些挑战。
原文摘要 · Abstract (English)
Orientation estimation is a fundamental task in 3D shape analysis which consists of estimating a shape's orientation axes: its side-, up-, and front-axes. Using this data, one can rotate a shape into canonical orientation, where its orientation axes are aligned with the coordinate axes. Developing an orientation algorithm that reliably estimates complete orientations of general shapes remains an open problem. We introduce a two-stage orientation pipeline that achieves state of the art performance on up-axis estimation and further demonstrate its efficacy on full-orientation estimation, where one seeks all three orientation axes. Unlike previous work, we train and evaluate our method on all of Shapenet rather than a subset of classes. We motivate our engineering contributions by theory describing fundamental obstacles to orientation estimation for rotationally-symmetric shapes, and show how our method avoids these obstacles.
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